//! 基于机器学习的内存和显存配置优化器实现

use core::sync::atomic::{AtomicBool, AtomicUsize, Ordering};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::Instant;
use std::sync::atomic::AtomicU64;

// 自定义AtomicF64实现
struct AtomicF64(AtomicU64);

impl AtomicF64 {
    pub fn new(value: f64) -> Self {
        AtomicF64(AtomicU64::new(value.to_bits()))
    }
    
    pub fn load(&self, ordering: Ordering) -> f64 {
        f64::from_bits(self.0.load(ordering))
    }
    
    pub fn store(&self, value: f64, ordering: Ordering) {
        self.0.store(value.to_bits(), ordering)
    }
}
use crate::core::error::FFIResult;
use super::super::cache::three_level_cache::{CacheLevel, ThreeLevelCache};

/// 优化历史记录
struct OptimizationHistory {
    /// 优化时间
    timestamp: Instant,
    /// 内存使用率
    memory_usage: f64,
    /// 显存使用率
    video_memory_usage: f64,
    /// 缓存命中率
    cache_hit_rate: f64,
    /// 系统响应时间
    response_time: f64,
    /// 碎片化程度
    fragmentation: f64,
    /// 优化评分
    score: u8,
}

/// 配置优化器
pub struct ConfigOptimizer {
    /// 优化历史记录
    history: Arc<RwLock<VecDeque<OptimizationHistory>>>,
    /// 最大历史记录数量
    max_history: usize,
    /// 学习率
    learning_rate: AtomicF64,
    /// 优化激进程度
    exploration_rate: AtomicF64,
    /// 是否启用深度学习模式
    deep_learning_enabled: AtomicBool,
    /// 性能指标权重
    performance_weights: Arc<Mutex<HashMap<String, f64>>>,
    /// 当前优化周期
    current_cycle: AtomicUsize,
}

impl ConfigOptimizer {
    /// 创建新的配置优化器
    pub fn new() -> Self {
        let mut weights = HashMap::new();
        weights.insert("memory_usage".to_string(), 0.25);
        weights.insert("video_memory_usage".to_string(), 0.25);
        weights.insert("cache_hit_rate".to_string(), 0.3);
        weights.insert("response_time".to_string(), 0.15);
        weights.insert("fragmentation".to_string(), 0.05);
        
        ConfigOptimizer {
            history: Arc::new(RwLock::new(VecDeque::with_capacity(100))),
            max_history: 100,
            learning_rate: AtomicF64::new(0.1),
            exploration_rate: AtomicF64::new(0.2),
            deep_learning_enabled: AtomicBool::new(true),
            performance_weights: Arc::new(Mutex::new(weights)),
            current_cycle: AtomicUsize::new(0),
        }
    }
    
    /// 优化配置
    pub fn optimize_config(&self, 
                          memory_usage: f64, 
                          video_memory_usage: f64, 
                          cache_hit_rate: f64, 
                          response_time: f64, 
                          fragmentation: f64) -> FFIResult<HashMap<String, String>> {
        // 计算优化评分
        let score = self.evaluate_optimization(
            memory_usage, 
            video_memory_usage, 
            cache_hit_rate, 
            response_time, 
            fragmentation
        );
        
        // 记录优化历史
        self.record_history(
            memory_usage, 
            video_memory_usage, 
            cache_hit_rate, 
            response_time, 
            fragmentation, 
            score
        );
        
        // 根据评分更新学习参数
        self.update_learning_parameters(score);
        
        // 生成优化建议
        let mut recommendations = HashMap::new();
        
        // 优化内存分配策略
        if memory_usage > 0.8 {
            recommendations.insert("memory_policy".to_string(), "aggressive_reclaim".to_string());
        } else if memory_usage < 0.3 {
            recommendations.insert("memory_policy".to_string(), "aggressive_allocation".to_string());
        } else {
            recommendations.insert("memory_policy".to_string(), "balanced".to_string());
        }
        
        // 优化显存分配策略
        if video_memory_usage > 0.85 {
            recommendations.insert("video_memory_policy".to_string(), "texture_compression".to_string());
        } else if video_memory_usage < 0.4 {
            recommendations.insert("video_memory_policy".to_string(), "prefetch_resources".to_string());
        }
        
        // 优化缓存策略
        if cache_hit_rate < 0.6 {
            recommendations.insert("cache_policy".to_string(), "increase_l1_l2_size".to_string());
        } else if cache_hit_rate > 0.9 {
            recommendations.insert("cache_policy".to_string(), "optimize_eviction".to_string());
        }
        
        // 优化碎片化
        if fragmentation > 0.3 {
            recommendations.insert("fragmentation_policy".to_string(), "defragmentation".to_string());
        }
        
        // 如果启用了深度学习模式，使用更复杂的优化策略
        if self.deep_learning_enabled.load(Ordering::Relaxed) {
            self.apply_deep_learning_optimization(&mut recommendations);
        }
        
        // 增加优化周期
        self.current_cycle.fetch_add(1, Ordering::Relaxed);
        
        Ok(recommendations)
    }
    
    /// 评估优化效果
    fn evaluate_optimization(&self, 
                            memory_usage: f64, 
                            video_memory_usage: f64, 
                            cache_hit_rate: f64, 
                            response_time: f64, 
                            fragmentation: f64) -> u8 {
        // 归一化各指标（确保在0-1范围内）
        let normalized_memory = 1.0 - memory_usage.min(1.0);
        let normalized_video_memory = 1.0 - video_memory_usage.min(1.0);
        let normalized_cache_hit = cache_hit_rate.min(1.0);
        let normalized_response = 1.0 - (response_time / 1000.0).min(1.0); // 假设1秒是最大响应时间
        let normalized_fragmentation = 1.0 - fragmentation.min(1.0);
        
        // 获取权重
        let weights = self.performance_weights.lock().unwrap();
        let memory_weight = *weights.get("memory_usage").unwrap_or(&0.25);
        let video_memory_weight = *weights.get("video_memory_usage").unwrap_or(&0.25);
        let cache_hit_weight = *weights.get("cache_hit_rate").unwrap_or(&0.3);
        let response_weight = *weights.get("response_time").unwrap_or(&0.15);
        let fragmentation_weight = *weights.get("fragmentation").unwrap_or(&0.05);
        
        // 计算加权得分
        let weighted_score = 
            normalized_memory * memory_weight +
            normalized_video_memory * video_memory_weight +
            normalized_cache_hit * cache_hit_weight +
            normalized_response * response_weight +
            normalized_fragmentation * fragmentation_weight;
        
        // 转换为0-100分
        (weighted_score * 100.0).round() as u8
    }
    
    /// 记录优化历史
    fn record_history(&self, 
                     memory_usage: f64, 
                     video_memory_usage: f64, 
                     cache_hit_rate: f64, 
                     response_time: f64, 
                     fragmentation: f64, 
                     score: u8) {
        let history_item = OptimizationHistory {
            timestamp: Instant::now(),
            memory_usage,
            video_memory_usage,
            cache_hit_rate,
            response_time,
            fragmentation,
            score,
        };
        
        if let Some(mut history_guard) = self.history.write().ok() {
            // 如果历史记录数量超过最大值，移除最旧的记录
            if history_guard.len() >= self.max_history {
                history_guard.pop_front();
            }
            
            // 添加新的历史记录
            history_guard.push_back(history_item);
        }
    }
    
    /// 更新学习参数
    fn update_learning_parameters(&self, score: u8) {
        // 根据评分动态调整学习率
        let current_learning_rate = self.learning_rate.load(Ordering::Relaxed);
        let current_exploration_rate = self.exploration_rate.load(Ordering::Relaxed);
        
        // 如果评分很高，降低学习率和探索率
        if score > 90 {
            self.learning_rate.store(current_learning_rate * 0.9, Ordering::Relaxed);
            self.exploration_rate.store(current_exploration_rate * 0.8, Ordering::Relaxed);
        }
        // 如果评分很低，增加学习率和探索率
        else if score < 50 {
            self.learning_rate.store((current_learning_rate * 1.1).min(0.5), Ordering::Relaxed);
            self.exploration_rate.store((current_exploration_rate * 1.2).min(0.5), Ordering::Relaxed);
        }
        // 否则保持相对稳定
        else {
            self.learning_rate.store(current_learning_rate * 0.95, Ordering::Relaxed);
            self.exploration_rate.store(current_exploration_rate * 0.95, Ordering::Relaxed);
        }
        
        // 确保学习率和探索率在合理范围内
        let learning_rate = self.learning_rate.load(Ordering::Relaxed);
        let exploration_rate = self.exploration_rate.load(Ordering::Relaxed);
        
        if learning_rate < 0.01 {
            self.learning_rate.store(0.01, Ordering::Relaxed);
        }
        if exploration_rate < 0.05 {
            self.exploration_rate.store(0.05, Ordering::Relaxed);
        }
    }
    
    /// 应用深度学习优化
    fn apply_deep_learning_optimization(&self, recommendations: &mut HashMap<String, String>) {
        // 这里可以实现更复杂的深度学习优化逻辑
        // 简化实现：基于历史数据进行预测和优化
        if let Some(history_guard) = self.history.read().ok() {
            if history_guard.len() > 10 {
                // 计算最近历史记录的平均评分
                let recent_history: Vec<_> = history_guard.iter().rev().take(10).collect();
                let avg_score: f64 = recent_history.iter().map(|h| h.score as f64).sum::<f64>() / 10.0;
                
                // 如果平均评分较低，尝试更激进的优化策略
                if avg_score < 70.0 {
                    recommendations.insert("deep_learning_mode".to_string(), "aggressive".to_string());
                }
                // 如果平均评分较高，保持当前策略
                else if avg_score > 90.0 {
                    recommendations.insert("deep_learning_mode".to_string(), "exploit".to_string());
                }
                // 否则继续探索
                else {
                    recommendations.insert("deep_learning_mode".to_string(), "explore".to_string());
                }
            }
        }
    }
    
    /// 优化整数类型的配置
    pub fn optimize_integer(&self, name: &str, current_value: usize, min_value: usize, max_value: usize) -> usize {
        // 基于当前状态和历史数据优化整数值
        // 简化实现：根据探索率决定是否尝试新值
        let exploration_rate = self.exploration_rate.load(Ordering::Relaxed);
        
        // 使用当前周期数作为简单的伪随机因子
        let cycle = self.current_cycle.load(Ordering::Relaxed);
        let pseudo_random = (cycle.wrapping_mul(1103515245).wrapping_add(12345)) % 2147483648;
        let pseudo_random_f64 = pseudo_random as f64 / 2147483648.0;
        
        if pseudo_random_f64 < exploration_rate {
            // 探索：随机选择一个新值
            let range = max_value - min_value;
            if range > 0 {
                min_value + (pseudo_random % range)
            } else {
                min_value
            }
        } else {
            // 利用：使用当前值或基于历史数据进行微调
            current_value
        }
    }
    
    /// 优化浮点数类型的配置
    pub fn optimize_float(&self, name: &str, current_value: f64, min_value: f64, max_value: f64) -> f64 {
        // 基于当前状态和历史数据优化浮点数值
        let exploration_rate = self.exploration_rate.load(Ordering::Relaxed);
        
        // 使用当前周期数作为简单的伪随机因子
        let cycle = self.current_cycle.load(Ordering::Relaxed);
        let pseudo_random = (cycle.wrapping_mul(1103515245).wrapping_add(12345)) % 2147483648;
        let pseudo_random_f64 = pseudo_random as f64 / 2147483648.0;
        
        if pseudo_random_f64 < exploration_rate {
            // 探索：随机选择一个新值
            min_value + (pseudo_random_f64 * (max_value - min_value))
        } else {
            // 利用：使用当前值或基于历史数据进行微调
            current_value
        }
    }
    
    /// 优化内存地址配置
    pub fn optimize_memory_address(&self, current_address: u64, alignment: u64) -> u64 {
        // 确保内存地址对齐
        (current_address + alignment - 1) & !(alignment - 1)
    }
    
    /// 优化显存地址配置
    pub fn optimize_video_memory_address(&self, current_address: u64, alignment: u64) -> u64 {
        // 确保显存地址对齐
        self.optimize_memory_address(current_address, alignment)
    }
    
    /// 切换深度学习模式
    pub fn toggle_deep_learning(&self, enabled: bool) {
        self.deep_learning_enabled.store(enabled, Ordering::Relaxed);
    }
    
    /// 获取优化历史
    pub fn get_history(&self) -> Vec<(Instant, u8)> {
        if let Some(history_guard) = self.history.read().ok() {
            history_guard.iter()
                .map(|h| (h.timestamp, h.score))
                .collect()
        } else {
            Vec::new()
        }
    }
    
    /// 重置优化历史
    pub fn reset_history(&self) {
        if let Some(mut history_guard) = self.history.write().ok() {
            history_guard.clear();
        }
        self.current_cycle.store(0, Ordering::Relaxed);
    }
    
    /// 获取学习率
    pub fn get_learning_rate(&self) -> f64 {
        self.learning_rate.load(Ordering::Relaxed)
    }
    
    /// 设置学习率
    pub fn set_learning_rate(&self, rate: f64) {
        if rate >= 0.01 && rate <= 0.5 {
            self.learning_rate.store(rate, Ordering::Relaxed);
        }
    }
    
    /// 获取探索率
    pub fn get_exploration_rate(&self) -> f64 {
        self.exploration_rate.load(Ordering::Relaxed)
    }
    
    /// 设置探索率
    pub fn set_exploration_rate(&self, rate: f64) {
        if rate >= 0.05 && rate <= 0.5 {
            self.exploration_rate.store(rate, Ordering::Relaxed);
        }
    }
    
    /// 获取当前优化周期
    pub fn get_current_cycle(&self) -> usize {
        self.current_cycle.load(Ordering::Relaxed)
    }
    
    /// 设置性能指标权重
    pub fn set_performance_weight(&self, metric: &str, weight: f64) -> bool {
        if let Some(mut weights_guard) = self.performance_weights.lock().ok() {
            if weights_guard.contains_key(metric) && weight >= 0.0 && weight <= 1.0 {
                weights_guard.insert(metric.to_string(), weight);
                
                // 重新归一化权重
                let total_weight: f64 = weights_guard.values().sum();
                if total_weight > 0.0 {
                    for (_, w) in weights_guard.iter_mut() {
                        *w /= total_weight;
                    }
                }
                
                true
            } else {
                false
            }
        } else {
            false
        }
    }
    
    /// 获取性能指标权重
    pub fn get_performance_weight(&self, metric: &str) -> Option<f64> {
        if let Some(weights_guard) = self.performance_weights.lock().ok() {
            weights_guard.get(metric).copied()
        } else {
            None
        }
    }
    
    /// 优化三级缓存配置
    pub fn optimize_three_level_cache(&self, cache: &ThreeLevelCache<Vec<u8>>) {
        // 获取缓存统计信息
        let (l1_size, l1_capacity, l1_usage) = cache.get_level_stats(CacheLevel::Level1);
        let (l2_size, l2_capacity, l2_usage) = cache.get_level_stats(CacheLevel::Level2);
        let (l3_size, l3_capacity, l3_usage) = cache.get_level_stats(CacheLevel::Level3);
        let (_, _, hit_rate) = cache.get_stats();
        
        // 基于使用率和命中率动态调整缓存大小
        // 这里可以实现更复杂的调整逻辑
        if l1_usage > 90.0 && hit_rate < 70.0 {
            // L1缓存使用率高但命中率低，增加L1容量
            let new_capacity = (l1_capacity as f64 * 1.2) as usize;
            cache.smart_resize(CacheLevel::Level1, new_capacity);
        } else if l1_usage < 20.0 {
            // L1缓存使用率低，减小L1容量
            let new_capacity = (l1_capacity as f64 * 0.8).max(10.0) as usize;
            cache.smart_resize(CacheLevel::Level1, new_capacity);
        }
        
        // 类似地调整L2和L3缓存
        if l2_usage > 85.0 {
            let new_capacity = (l2_capacity as f64 * 1.15) as usize;
            cache.smart_resize(CacheLevel::Level2, new_capacity);
        } else if l2_usage < 15.0 {
            let new_capacity = (l2_capacity as f64 * 0.85).max(20.0) as usize;
            cache.smart_resize(CacheLevel::Level2, new_capacity);
        }
        
        if l3_usage > 80.0 {
            let new_capacity = (l3_capacity as f64 * 1.1) as usize;
            cache.smart_resize(CacheLevel::Level3, new_capacity);
        } else if l3_usage < 10.0 {
            let new_capacity = (l3_capacity as f64 * 0.9).max(50.0) as usize;
            cache.smart_resize(CacheLevel::Level3, new_capacity);
        }
    }
}